Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded
Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)These scripts will load:
.R files inside /scripts/.R files inside /src/These scripts also contain routines to access the latest clean encrypted data (see next section).
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 219,968 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 219,958 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 7
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
## 140 2020-07-18 East of England 0
## 141 2020-07-19 East of England 1
## 142 2020-07-20 East of England 1
## 143 2020-07-21 East of England 1
## 144 2020-07-22 East of England 1
## 145 2020-07-23 East of England 1
## 146 2020-07-24 East of England 1
## 147 2020-07-25 East of England 0
## 148 2020-07-26 East of England 1
## 149 2020-07-27 East of England 1
## 150 2020-07-28 East of England 1
## 151 2020-07-29 East of England 0
## 152 2020-07-30 East of England 0
## 153 2020-07-31 East of England 1
## 154 2020-08-01 East of England 0
## 155 2020-08-02 East of England 0
## 156 2020-08-03 East of England 0
## 157 2020-08-04 East of England 1
## 158 2020-08-05 East of England 1
## 159 2020-08-06 East of England 0
## 160 2020-08-07 East of England 1
## 161 2020-08-08 East of England 0
## 162 2020-08-09 East of England 0
## 163 2020-08-10 East of England 1
## 164 2020-08-11 East of England 2
## 165 2020-08-12 East of England 1
## 166 2020-08-13 East of England 0
## 167 2020-08-14 East of England 0
## 168 2020-08-15 East of England 1
## 169 2020-08-16 East of England 0
## 170 2020-08-17 East of England 0
## 171 2020-08-18 East of England 2
## 172 2020-08-19 East of England 1
## 173 2020-08-20 East of England 1
## 174 2020-08-21 East of England 0
## 175 2020-08-22 East of England 0
## 176 2020-08-23 East of England 1
## 177 2020-08-24 East of England 0
## 178 2020-03-01 London 0
## 179 2020-03-02 London 0
## 180 2020-03-03 London 0
## 181 2020-03-04 London 0
## 182 2020-03-05 London 0
## 183 2020-03-06 London 1
## 184 2020-03-07 London 0
## 185 2020-03-08 London 0
## 186 2020-03-09 London 1
## 187 2020-03-10 London 0
## 188 2020-03-11 London 5
## 189 2020-03-12 London 6
## 190 2020-03-13 London 10
## 191 2020-03-14 London 13
## 192 2020-03-15 London 9
## 193 2020-03-16 London 15
## 194 2020-03-17 London 23
## 195 2020-03-18 London 28
## 196 2020-03-19 London 25
## 197 2020-03-20 London 44
## 198 2020-03-21 London 49
## 199 2020-03-22 London 54
## 200 2020-03-23 London 63
## 201 2020-03-24 London 86
## 202 2020-03-25 London 112
## 203 2020-03-26 London 129
## 204 2020-03-27 London 129
## 205 2020-03-28 London 122
## 206 2020-03-29 London 145
## 207 2020-03-30 London 149
## 208 2020-03-31 London 181
## 209 2020-04-01 London 202
## 210 2020-04-02 London 191
## 211 2020-04-03 London 198
## 212 2020-04-04 London 231
## 213 2020-04-05 London 195
## 214 2020-04-06 London 197
## 215 2020-04-07 London 220
## 216 2020-04-08 London 239
## 217 2020-04-09 London 206
## 218 2020-04-10 London 171
## 219 2020-04-11 London 178
## 220 2020-04-12 London 158
## 221 2020-04-13 London 166
## 222 2020-04-14 London 143
## 223 2020-04-15 London 142
## 224 2020-04-16 London 140
## 225 2020-04-17 London 100
## 226 2020-04-18 London 101
## 227 2020-04-19 London 103
## 228 2020-04-20 London 96
## 229 2020-04-21 London 94
## 230 2020-04-22 London 109
## 231 2020-04-23 London 77
## 232 2020-04-24 London 71
## 233 2020-04-25 London 58
## 234 2020-04-26 London 53
## 235 2020-04-27 London 51
## 236 2020-04-28 London 44
## 237 2020-04-29 London 45
## 238 2020-04-30 London 40
## 239 2020-05-01 London 41
## 240 2020-05-02 London 41
## 241 2020-05-03 London 36
## 242 2020-05-04 London 30
## 243 2020-05-05 London 25
## 244 2020-05-06 London 37
## 245 2020-05-07 London 37
## 246 2020-05-08 London 30
## 247 2020-05-09 London 23
## 248 2020-05-10 London 26
## 249 2020-05-11 London 18
## 250 2020-05-12 London 18
## 251 2020-05-13 London 17
## 252 2020-05-14 London 20
## 253 2020-05-15 London 18
## 254 2020-05-16 London 14
## 255 2020-05-17 London 15
## 256 2020-05-18 London 11
## 257 2020-05-19 London 14
## 258 2020-05-20 London 19
## 259 2020-05-21 London 12
## 260 2020-05-22 London 10
## 261 2020-05-23 London 6
## 262 2020-05-24 London 7
## 263 2020-05-25 London 9
## 264 2020-05-26 London 14
## 265 2020-05-27 London 7
## 266 2020-05-28 London 8
## 267 2020-05-29 London 7
## 268 2020-05-30 London 12
## 269 2020-05-31 London 6
## 270 2020-06-01 London 10
## 271 2020-06-02 London 8
## 272 2020-06-03 London 6
## 273 2020-06-04 London 8
## 274 2020-06-05 London 4
## 275 2020-06-06 London 0
## 276 2020-06-07 London 5
## 277 2020-06-08 London 5
## 278 2020-06-09 London 5
## 279 2020-06-10 London 8
## 280 2020-06-11 London 5
## 281 2020-06-12 London 3
## 282 2020-06-13 London 3
## 283 2020-06-14 London 3
## 284 2020-06-15 London 1
## 285 2020-06-16 London 2
## 286 2020-06-17 London 1
## 287 2020-06-18 London 2
## 288 2020-06-19 London 5
## 289 2020-06-20 London 3
## 290 2020-06-21 London 4
## 291 2020-06-22 London 2
## 292 2020-06-23 London 1
## 293 2020-06-24 London 4
## 294 2020-06-25 London 3
## 295 2020-06-26 London 2
## 296 2020-06-27 London 1
## 297 2020-06-28 London 2
## 298 2020-06-29 London 2
## 299 2020-06-30 London 1
## 300 2020-07-01 London 2
## 301 2020-07-02 London 2
## 302 2020-07-03 London 2
## 303 2020-07-04 London 1
## 304 2020-07-05 London 3
## 305 2020-07-06 London 2
## 306 2020-07-07 London 1
## 307 2020-07-08 London 3
## 308 2020-07-09 London 4
## 309 2020-07-10 London 0
## 310 2020-07-11 London 1
## 311 2020-07-12 London 1
## 312 2020-07-13 London 1
## 313 2020-07-14 London 0
## 314 2020-07-15 London 2
## 315 2020-07-16 London 0
## 316 2020-07-17 London 0
## 317 2020-07-18 London 2
## 318 2020-07-19 London 0
## 319 2020-07-20 London 0
## 320 2020-07-21 London 1
## 321 2020-07-22 London 0
## 322 2020-07-23 London 2
## 323 2020-07-24 London 0
## 324 2020-07-25 London 1
## 325 2020-07-26 London 0
## 326 2020-07-27 London 1
## 327 2020-07-28 London 0
## 328 2020-07-29 London 0
## 329 2020-07-30 London 0
## 330 2020-07-31 London 0
## 331 2020-08-01 London 0
## 332 2020-08-02 London 2
## 333 2020-08-03 London 0
## 334 2020-08-04 London 0
## 335 2020-08-05 London 0
## 336 2020-08-06 London 1
## 337 2020-08-07 London 0
## 338 2020-08-08 London 0
## 339 2020-08-09 London 0
## 340 2020-08-10 London 0
## 341 2020-08-11 London 1
## 342 2020-08-12 London 0
## 343 2020-08-13 London 2
## 344 2020-08-14 London 0
## 345 2020-08-15 London 0
## 346 2020-08-16 London 0
## 347 2020-08-17 London 1
## 348 2020-08-18 London 1
## 349 2020-08-19 London 0
## 350 2020-08-20 London 1
## 351 2020-08-21 London 0
## 352 2020-08-22 London 0
## 353 2020-08-23 London 0
## 354 2020-08-24 London 0
## 355 2020-03-01 Midlands 0
## 356 2020-03-02 Midlands 0
## 357 2020-03-03 Midlands 1
## 358 2020-03-04 Midlands 0
## 359 2020-03-05 Midlands 0
## 360 2020-03-06 Midlands 0
## 361 2020-03-07 Midlands 0
## 362 2020-03-08 Midlands 2
## 363 2020-03-09 Midlands 1
## 364 2020-03-10 Midlands 0
## 365 2020-03-11 Midlands 2
## 366 2020-03-12 Midlands 6
## 367 2020-03-13 Midlands 5
## 368 2020-03-14 Midlands 4
## 369 2020-03-15 Midlands 5
## 370 2020-03-16 Midlands 11
## 371 2020-03-17 Midlands 8
## 372 2020-03-18 Midlands 13
## 373 2020-03-19 Midlands 8
## 374 2020-03-20 Midlands 28
## 375 2020-03-21 Midlands 13
## 376 2020-03-22 Midlands 31
## 377 2020-03-23 Midlands 33
## 378 2020-03-24 Midlands 41
## 379 2020-03-25 Midlands 48
## 380 2020-03-26 Midlands 64
## 381 2020-03-27 Midlands 72
## 382 2020-03-28 Midlands 89
## 383 2020-03-29 Midlands 92
## 384 2020-03-30 Midlands 90
## 385 2020-03-31 Midlands 123
## 386 2020-04-01 Midlands 140
## 387 2020-04-02 Midlands 142
## 388 2020-04-03 Midlands 124
## 389 2020-04-04 Midlands 151
## 390 2020-04-05 Midlands 164
## 391 2020-04-06 Midlands 140
## 392 2020-04-07 Midlands 123
## 393 2020-04-08 Midlands 186
## 394 2020-04-09 Midlands 139
## 395 2020-04-10 Midlands 127
## 396 2020-04-11 Midlands 142
## 397 2020-04-12 Midlands 139
## 398 2020-04-13 Midlands 120
## 399 2020-04-14 Midlands 116
## 400 2020-04-15 Midlands 147
## 401 2020-04-16 Midlands 102
## 402 2020-04-17 Midlands 118
## 403 2020-04-18 Midlands 115
## 404 2020-04-19 Midlands 92
## 405 2020-04-20 Midlands 107
## 406 2020-04-21 Midlands 86
## 407 2020-04-22 Midlands 78
## 408 2020-04-23 Midlands 103
## 409 2020-04-24 Midlands 79
## 410 2020-04-25 Midlands 72
## 411 2020-04-26 Midlands 81
## 412 2020-04-27 Midlands 74
## 413 2020-04-28 Midlands 68
## 414 2020-04-29 Midlands 53
## 415 2020-04-30 Midlands 56
## 416 2020-05-01 Midlands 64
## 417 2020-05-02 Midlands 51
## 418 2020-05-03 Midlands 52
## 419 2020-05-04 Midlands 61
## 420 2020-05-05 Midlands 59
## 421 2020-05-06 Midlands 59
## 422 2020-05-07 Midlands 48
## 423 2020-05-08 Midlands 34
## 424 2020-05-09 Midlands 37
## 425 2020-05-10 Midlands 42
## 426 2020-05-11 Midlands 33
## 427 2020-05-12 Midlands 45
## 428 2020-05-13 Midlands 40
## 429 2020-05-14 Midlands 38
## 430 2020-05-15 Midlands 40
## 431 2020-05-16 Midlands 34
## 432 2020-05-17 Midlands 31
## 433 2020-05-18 Midlands 36
## 434 2020-05-19 Midlands 35
## 435 2020-05-20 Midlands 36
## 436 2020-05-21 Midlands 32
## 437 2020-05-22 Midlands 27
## 438 2020-05-23 Midlands 34
## 439 2020-05-24 Midlands 20
## 440 2020-05-25 Midlands 26
## 441 2020-05-26 Midlands 33
## 442 2020-05-27 Midlands 29
## 443 2020-05-28 Midlands 28
## 444 2020-05-29 Midlands 20
## 445 2020-05-30 Midlands 21
## 446 2020-05-31 Midlands 22
## 447 2020-06-01 Midlands 20
## 448 2020-06-02 Midlands 22
## 449 2020-06-03 Midlands 24
## 450 2020-06-04 Midlands 16
## 451 2020-06-05 Midlands 21
## 452 2020-06-06 Midlands 20
## 453 2020-06-07 Midlands 17
## 454 2020-06-08 Midlands 16
## 455 2020-06-09 Midlands 18
## 456 2020-06-10 Midlands 15
## 457 2020-06-11 Midlands 13
## 458 2020-06-12 Midlands 12
## 459 2020-06-13 Midlands 6
## 460 2020-06-14 Midlands 18
## 461 2020-06-15 Midlands 12
## 462 2020-06-16 Midlands 15
## 463 2020-06-17 Midlands 11
## 464 2020-06-18 Midlands 15
## 465 2020-06-19 Midlands 10
## 466 2020-06-20 Midlands 15
## 467 2020-06-21 Midlands 14
## 468 2020-06-22 Midlands 14
## 469 2020-06-23 Midlands 16
## 470 2020-06-24 Midlands 15
## 471 2020-06-25 Midlands 18
## 472 2020-06-26 Midlands 5
## 473 2020-06-27 Midlands 5
## 474 2020-06-28 Midlands 7
## 475 2020-06-29 Midlands 6
## 476 2020-06-30 Midlands 6
## 477 2020-07-01 Midlands 7
## 478 2020-07-02 Midlands 10
## 479 2020-07-03 Midlands 3
## 480 2020-07-04 Midlands 4
## 481 2020-07-05 Midlands 6
## 482 2020-07-06 Midlands 5
## 483 2020-07-07 Midlands 3
## 484 2020-07-08 Midlands 5
## 485 2020-07-09 Midlands 9
## 486 2020-07-10 Midlands 3
## 487 2020-07-11 Midlands 0
## 488 2020-07-12 Midlands 5
## 489 2020-07-13 Midlands 1
## 490 2020-07-14 Midlands 1
## 491 2020-07-15 Midlands 6
## 492 2020-07-16 Midlands 2
## 493 2020-07-17 Midlands 3
## 494 2020-07-18 Midlands 3
## 495 2020-07-19 Midlands 3
## 496 2020-07-20 Midlands 3
## 497 2020-07-21 Midlands 1
## 498 2020-07-22 Midlands 2
## 499 2020-07-23 Midlands 6
## 500 2020-07-24 Midlands 1
## 501 2020-07-25 Midlands 4
## 502 2020-07-26 Midlands 4
## 503 2020-07-27 Midlands 5
## 504 2020-07-28 Midlands 1
## 505 2020-07-29 Midlands 1
## 506 2020-07-30 Midlands 1
## 507 2020-07-31 Midlands 1
## 508 2020-08-01 Midlands 0
## 509 2020-08-02 Midlands 1
## 510 2020-08-03 Midlands 2
## 511 2020-08-04 Midlands 1
## 512 2020-08-05 Midlands 1
## 513 2020-08-06 Midlands 0
## 514 2020-08-07 Midlands 3
## 515 2020-08-08 Midlands 2
## 516 2020-08-09 Midlands 0
## 517 2020-08-10 Midlands 0
## 518 2020-08-11 Midlands 2
## 519 2020-08-12 Midlands 0
## 520 2020-08-13 Midlands 0
## 521 2020-08-14 Midlands 0
## 522 2020-08-15 Midlands 1
## 523 2020-08-16 Midlands 0
## 524 2020-08-17 Midlands 0
## 525 2020-08-18 Midlands 0
## 526 2020-08-19 Midlands 0
## 527 2020-08-20 Midlands 0
## 528 2020-08-21 Midlands 1
## 529 2020-08-22 Midlands 0
## 530 2020-08-23 Midlands 0
## 531 2020-08-24 Midlands 0
## 532 2020-03-01 North East and Yorkshire 0
## 533 2020-03-02 North East and Yorkshire 0
## 534 2020-03-03 North East and Yorkshire 0
## 535 2020-03-04 North East and Yorkshire 0
## 536 2020-03-05 North East and Yorkshire 0
## 537 2020-03-06 North East and Yorkshire 0
## 538 2020-03-07 North East and Yorkshire 0
## 539 2020-03-08 North East and Yorkshire 0
## 540 2020-03-09 North East and Yorkshire 0
## 541 2020-03-10 North East and Yorkshire 0
## 542 2020-03-11 North East and Yorkshire 0
## 543 2020-03-12 North East and Yorkshire 0
## 544 2020-03-13 North East and Yorkshire 0
## 545 2020-03-14 North East and Yorkshire 0
## 546 2020-03-15 North East and Yorkshire 2
## 547 2020-03-16 North East and Yorkshire 3
## 548 2020-03-17 North East and Yorkshire 1
## 549 2020-03-18 North East and Yorkshire 2
## 550 2020-03-19 North East and Yorkshire 6
## 551 2020-03-20 North East and Yorkshire 5
## 552 2020-03-21 North East and Yorkshire 6
## 553 2020-03-22 North East and Yorkshire 7
## 554 2020-03-23 North East and Yorkshire 9
## 555 2020-03-24 North East and Yorkshire 8
## 556 2020-03-25 North East and Yorkshire 18
## 557 2020-03-26 North East and Yorkshire 21
## 558 2020-03-27 North East and Yorkshire 28
## 559 2020-03-28 North East and Yorkshire 35
## 560 2020-03-29 North East and Yorkshire 38
## 561 2020-03-30 North East and Yorkshire 64
## 562 2020-03-31 North East and Yorkshire 60
## 563 2020-04-01 North East and Yorkshire 67
## 564 2020-04-02 North East and Yorkshire 75
## 565 2020-04-03 North East and Yorkshire 100
## 566 2020-04-04 North East and Yorkshire 105
## 567 2020-04-05 North East and Yorkshire 92
## 568 2020-04-06 North East and Yorkshire 96
## 569 2020-04-07 North East and Yorkshire 102
## 570 2020-04-08 North East and Yorkshire 107
## 571 2020-04-09 North East and Yorkshire 111
## 572 2020-04-10 North East and Yorkshire 117
## 573 2020-04-11 North East and Yorkshire 98
## 574 2020-04-12 North East and Yorkshire 84
## 575 2020-04-13 North East and Yorkshire 94
## 576 2020-04-14 North East and Yorkshire 107
## 577 2020-04-15 North East and Yorkshire 96
## 578 2020-04-16 North East and Yorkshire 103
## 579 2020-04-17 North East and Yorkshire 88
## 580 2020-04-18 North East and Yorkshire 95
## 581 2020-04-19 North East and Yorkshire 88
## 582 2020-04-20 North East and Yorkshire 100
## 583 2020-04-21 North East and Yorkshire 76
## 584 2020-04-22 North East and Yorkshire 84
## 585 2020-04-23 North East and Yorkshire 63
## 586 2020-04-24 North East and Yorkshire 72
## 587 2020-04-25 North East and Yorkshire 69
## 588 2020-04-26 North East and Yorkshire 65
## 589 2020-04-27 North East and Yorkshire 65
## 590 2020-04-28 North East and Yorkshire 57
## 591 2020-04-29 North East and Yorkshire 69
## 592 2020-04-30 North East and Yorkshire 57
## 593 2020-05-01 North East and Yorkshire 64
## 594 2020-05-02 North East and Yorkshire 48
## 595 2020-05-03 North East and Yorkshire 40
## 596 2020-05-04 North East and Yorkshire 49
## 597 2020-05-05 North East and Yorkshire 40
## 598 2020-05-06 North East and Yorkshire 51
## 599 2020-05-07 North East and Yorkshire 45
## 600 2020-05-08 North East and Yorkshire 42
## 601 2020-05-09 North East and Yorkshire 44
## 602 2020-05-10 North East and Yorkshire 40
## 603 2020-05-11 North East and Yorkshire 29
## 604 2020-05-12 North East and Yorkshire 27
## 605 2020-05-13 North East and Yorkshire 28
## 606 2020-05-14 North East and Yorkshire 31
## 607 2020-05-15 North East and Yorkshire 32
## 608 2020-05-16 North East and Yorkshire 35
## 609 2020-05-17 North East and Yorkshire 26
## 610 2020-05-18 North East and Yorkshire 30
## 611 2020-05-19 North East and Yorkshire 27
## 612 2020-05-20 North East and Yorkshire 22
## 613 2020-05-21 North East and Yorkshire 33
## 614 2020-05-22 North East and Yorkshire 22
## 615 2020-05-23 North East and Yorkshire 18
## 616 2020-05-24 North East and Yorkshire 26
## 617 2020-05-25 North East and Yorkshire 21
## 618 2020-05-26 North East and Yorkshire 21
## 619 2020-05-27 North East and Yorkshire 22
## 620 2020-05-28 North East and Yorkshire 21
## 621 2020-05-29 North East and Yorkshire 25
## 622 2020-05-30 North East and Yorkshire 20
## 623 2020-05-31 North East and Yorkshire 20
## 624 2020-06-01 North East and Yorkshire 17
## 625 2020-06-02 North East and Yorkshire 23
## 626 2020-06-03 North East and Yorkshire 23
## 627 2020-06-04 North East and Yorkshire 17
## 628 2020-06-05 North East and Yorkshire 18
## 629 2020-06-06 North East and Yorkshire 21
## 630 2020-06-07 North East and Yorkshire 14
## 631 2020-06-08 North East and Yorkshire 11
## 632 2020-06-09 North East and Yorkshire 12
## 633 2020-06-10 North East and Yorkshire 19
## 634 2020-06-11 North East and Yorkshire 7
## 635 2020-06-12 North East and Yorkshire 9
## 636 2020-06-13 North East and Yorkshire 10
## 637 2020-06-14 North East and Yorkshire 11
## 638 2020-06-15 North East and Yorkshire 9
## 639 2020-06-16 North East and Yorkshire 10
## 640 2020-06-17 North East and Yorkshire 9
## 641 2020-06-18 North East and Yorkshire 11
## 642 2020-06-19 North East and Yorkshire 6
## 643 2020-06-20 North East and Yorkshire 5
## 644 2020-06-21 North East and Yorkshire 4
## 645 2020-06-22 North East and Yorkshire 7
## 646 2020-06-23 North East and Yorkshire 8
## 647 2020-06-24 North East and Yorkshire 10
## 648 2020-06-25 North East and Yorkshire 4
## 649 2020-06-26 North East and Yorkshire 7
## 650 2020-06-27 North East and Yorkshire 4
## 651 2020-06-28 North East and Yorkshire 5
## 652 2020-06-29 North East and Yorkshire 2
## 653 2020-06-30 North East and Yorkshire 7
## 654 2020-07-01 North East and Yorkshire 1
## 655 2020-07-02 North East and Yorkshire 4
## 656 2020-07-03 North East and Yorkshire 4
## 657 2020-07-04 North East and Yorkshire 4
## 658 2020-07-05 North East and Yorkshire 3
## 659 2020-07-06 North East and Yorkshire 2
## 660 2020-07-07 North East and Yorkshire 3
## 661 2020-07-08 North East and Yorkshire 3
## 662 2020-07-09 North East and Yorkshire 0
## 663 2020-07-10 North East and Yorkshire 3
## 664 2020-07-11 North East and Yorkshire 1
## 665 2020-07-12 North East and Yorkshire 4
## 666 2020-07-13 North East and Yorkshire 1
## 667 2020-07-14 North East and Yorkshire 1
## 668 2020-07-15 North East and Yorkshire 2
## 669 2020-07-16 North East and Yorkshire 3
## 670 2020-07-17 North East and Yorkshire 1
## 671 2020-07-18 North East and Yorkshire 2
## 672 2020-07-19 North East and Yorkshire 2
## 673 2020-07-20 North East and Yorkshire 1
## 674 2020-07-21 North East and Yorkshire 1
## 675 2020-07-22 North East and Yorkshire 6
## 676 2020-07-23 North East and Yorkshire 0
## 677 2020-07-24 North East and Yorkshire 1
## 678 2020-07-25 North East and Yorkshire 5
## 679 2020-07-26 North East and Yorkshire 1
## 680 2020-07-27 North East and Yorkshire 0
## 681 2020-07-28 North East and Yorkshire 2
## 682 2020-07-29 North East and Yorkshire 1
## 683 2020-07-30 North East and Yorkshire 0
## 684 2020-07-31 North East and Yorkshire 1
## 685 2020-08-01 North East and Yorkshire 3
## 686 2020-08-02 North East and Yorkshire 2
## 687 2020-08-03 North East and Yorkshire 1
## 688 2020-08-04 North East and Yorkshire 2
## 689 2020-08-05 North East and Yorkshire 1
## 690 2020-08-06 North East and Yorkshire 4
## 691 2020-08-07 North East and Yorkshire 0
## 692 2020-08-08 North East and Yorkshire 1
## 693 2020-08-09 North East and Yorkshire 2
## 694 2020-08-10 North East and Yorkshire 2
## 695 2020-08-11 North East and Yorkshire 2
## 696 2020-08-12 North East and Yorkshire 2
## 697 2020-08-13 North East and Yorkshire 0
## 698 2020-08-14 North East and Yorkshire 1
## 699 2020-08-15 North East and Yorkshire 1
## 700 2020-08-16 North East and Yorkshire 0
## 701 2020-08-17 North East and Yorkshire 4
## 702 2020-08-18 North East and Yorkshire 1
## 703 2020-08-19 North East and Yorkshire 0
## 704 2020-08-20 North East and Yorkshire 0
## 705 2020-08-21 North East and Yorkshire 1
## 706 2020-08-22 North East and Yorkshire 1
## 707 2020-08-23 North East and Yorkshire 1
## 708 2020-08-24 North East and Yorkshire 0
## 709 2020-03-01 North West 0
## 710 2020-03-02 North West 0
## 711 2020-03-03 North West 0
## 712 2020-03-04 North West 0
## 713 2020-03-05 North West 1
## 714 2020-03-06 North West 0
## 715 2020-03-07 North West 0
## 716 2020-03-08 North West 1
## 717 2020-03-09 North West 0
## 718 2020-03-10 North West 0
## 719 2020-03-11 North West 0
## 720 2020-03-12 North West 2
## 721 2020-03-13 North West 3
## 722 2020-03-14 North West 1
## 723 2020-03-15 North West 4
## 724 2020-03-16 North West 2
## 725 2020-03-17 North West 4
## 726 2020-03-18 North West 6
## 727 2020-03-19 North West 7
## 728 2020-03-20 North West 10
## 729 2020-03-21 North West 11
## 730 2020-03-22 North West 13
## 731 2020-03-23 North West 15
## 732 2020-03-24 North West 21
## 733 2020-03-25 North West 21
## 734 2020-03-26 North West 29
## 735 2020-03-27 North West 36
## 736 2020-03-28 North West 28
## 737 2020-03-29 North West 46
## 738 2020-03-30 North West 67
## 739 2020-03-31 North West 52
## 740 2020-04-01 North West 86
## 741 2020-04-02 North West 96
## 742 2020-04-03 North West 95
## 743 2020-04-04 North West 98
## 744 2020-04-05 North West 102
## 745 2020-04-06 North West 100
## 746 2020-04-07 North West 135
## 747 2020-04-08 North West 127
## 748 2020-04-09 North West 119
## 749 2020-04-10 North West 117
## 750 2020-04-11 North West 138
## 751 2020-04-12 North West 125
## 752 2020-04-13 North West 129
## 753 2020-04-14 North West 131
## 754 2020-04-15 North West 114
## 755 2020-04-16 North West 135
## 756 2020-04-17 North West 98
## 757 2020-04-18 North West 113
## 758 2020-04-19 North West 71
## 759 2020-04-20 North West 83
## 760 2020-04-21 North West 76
## 761 2020-04-22 North West 86
## 762 2020-04-23 North West 85
## 763 2020-04-24 North West 66
## 764 2020-04-25 North West 66
## 765 2020-04-26 North West 55
## 766 2020-04-27 North West 54
## 767 2020-04-28 North West 57
## 768 2020-04-29 North West 63
## 769 2020-04-30 North West 59
## 770 2020-05-01 North West 45
## 771 2020-05-02 North West 56
## 772 2020-05-03 North West 55
## 773 2020-05-04 North West 48
## 774 2020-05-05 North West 48
## 775 2020-05-06 North West 44
## 776 2020-05-07 North West 49
## 777 2020-05-08 North West 42
## 778 2020-05-09 North West 31
## 779 2020-05-10 North West 42
## 780 2020-05-11 North West 35
## 781 2020-05-12 North West 38
## 782 2020-05-13 North West 25
## 783 2020-05-14 North West 26
## 784 2020-05-15 North West 33
## 785 2020-05-16 North West 32
## 786 2020-05-17 North West 24
## 787 2020-05-18 North West 31
## 788 2020-05-19 North West 35
## 789 2020-05-20 North West 27
## 790 2020-05-21 North West 27
## 791 2020-05-22 North West 26
## 792 2020-05-23 North West 31
## 793 2020-05-24 North West 26
## 794 2020-05-25 North West 31
## 795 2020-05-26 North West 27
## 796 2020-05-27 North West 27
## 797 2020-05-28 North West 28
## 798 2020-05-29 North West 20
## 799 2020-05-30 North West 19
## 800 2020-05-31 North West 13
## 801 2020-06-01 North West 12
## 802 2020-06-02 North West 27
## 803 2020-06-03 North West 22
## 804 2020-06-04 North West 22
## 805 2020-06-05 North West 16
## 806 2020-06-06 North West 26
## 807 2020-06-07 North West 20
## 808 2020-06-08 North West 23
## 809 2020-06-09 North West 17
## 810 2020-06-10 North West 16
## 811 2020-06-11 North West 16
## 812 2020-06-12 North West 11
## 813 2020-06-13 North West 10
## 814 2020-06-14 North West 15
## 815 2020-06-15 North West 16
## 816 2020-06-16 North West 15
## 817 2020-06-17 North West 13
## 818 2020-06-18 North West 14
## 819 2020-06-19 North West 7
## 820 2020-06-20 North West 11
## 821 2020-06-21 North West 8
## 822 2020-06-22 North West 11
## 823 2020-06-23 North West 13
## 824 2020-06-24 North West 13
## 825 2020-06-25 North West 15
## 826 2020-06-26 North West 6
## 827 2020-06-27 North West 7
## 828 2020-06-28 North West 9
## 829 2020-06-29 North West 9
## 830 2020-06-30 North West 7
## 831 2020-07-01 North West 3
## 832 2020-07-02 North West 6
## 833 2020-07-03 North West 7
## 834 2020-07-04 North West 4
## 835 2020-07-05 North West 6
## 836 2020-07-06 North West 9
## 837 2020-07-07 North West 8
## 838 2020-07-08 North West 5
## 839 2020-07-09 North West 10
## 840 2020-07-10 North West 2
## 841 2020-07-11 North West 5
## 842 2020-07-12 North West 0
## 843 2020-07-13 North West 6
## 844 2020-07-14 North West 4
## 845 2020-07-15 North West 5
## 846 2020-07-16 North West 2
## 847 2020-07-17 North West 4
## 848 2020-07-18 North West 5
## 849 2020-07-19 North West 3
## 850 2020-07-20 North West 0
## 851 2020-07-21 North West 2
## 852 2020-07-22 North West 3
## 853 2020-07-23 North West 3
## 854 2020-07-24 North West 1
## 855 2020-07-25 North West 0
## 856 2020-07-26 North West 3
## 857 2020-07-27 North West 1
## 858 2020-07-28 North West 1
## 859 2020-07-29 North West 2
## 860 2020-07-30 North West 1
## 861 2020-07-31 North West 0
## 862 2020-08-01 North West 2
## 863 2020-08-02 North West 0
## 864 2020-08-03 North West 7
## 865 2020-08-04 North West 3
## 866 2020-08-05 North West 2
## 867 2020-08-06 North West 1
## 868 2020-08-07 North West 0
## 869 2020-08-08 North West 2
## 870 2020-08-09 North West 3
## 871 2020-08-10 North West 2
## 872 2020-08-11 North West 3
## 873 2020-08-12 North West 0
## 874 2020-08-13 North West 2
## 875 2020-08-14 North West 2
## 876 2020-08-15 North West 5
## 877 2020-08-16 North West 1
## 878 2020-08-17 North West 1
## 879 2020-08-18 North West 2
## 880 2020-08-19 North West 0
## 881 2020-08-20 North West 1
## 882 2020-08-21 North West 3
## 883 2020-08-22 North West 2
## 884 2020-08-23 North West 3
## 885 2020-08-24 North West 2
## 886 2020-03-01 South East 0
## 887 2020-03-02 South East 0
## 888 2020-03-03 South East 1
## 889 2020-03-04 South East 0
## 890 2020-03-05 South East 1
## 891 2020-03-06 South East 0
## 892 2020-03-07 South East 0
## 893 2020-03-08 South East 1
## 894 2020-03-09 South East 1
## 895 2020-03-10 South East 1
## 896 2020-03-11 South East 1
## 897 2020-03-12 South East 0
## 898 2020-03-13 South East 1
## 899 2020-03-14 South East 1
## 900 2020-03-15 South East 5
## 901 2020-03-16 South East 8
## 902 2020-03-17 South East 7
## 903 2020-03-18 South East 10
## 904 2020-03-19 South East 9
## 905 2020-03-20 South East 13
## 906 2020-03-21 South East 7
## 907 2020-03-22 South East 25
## 908 2020-03-23 South East 20
## 909 2020-03-24 South East 22
## 910 2020-03-25 South East 29
## 911 2020-03-26 South East 35
## 912 2020-03-27 South East 34
## 913 2020-03-28 South East 36
## 914 2020-03-29 South East 55
## 915 2020-03-30 South East 58
## 916 2020-03-31 South East 65
## 917 2020-04-01 South East 66
## 918 2020-04-02 South East 55
## 919 2020-04-03 South East 72
## 920 2020-04-04 South East 80
## 921 2020-04-05 South East 82
## 922 2020-04-06 South East 88
## 923 2020-04-07 South East 100
## 924 2020-04-08 South East 83
## 925 2020-04-09 South East 104
## 926 2020-04-10 South East 88
## 927 2020-04-11 South East 88
## 928 2020-04-12 South East 88
## 929 2020-04-13 South East 84
## 930 2020-04-14 South East 65
## 931 2020-04-15 South East 72
## 932 2020-04-16 South East 56
## 933 2020-04-17 South East 86
## 934 2020-04-18 South East 57
## 935 2020-04-19 South East 70
## 936 2020-04-20 South East 87
## 937 2020-04-21 South East 51
## 938 2020-04-22 South East 54
## 939 2020-04-23 South East 57
## 940 2020-04-24 South East 64
## 941 2020-04-25 South East 51
## 942 2020-04-26 South East 51
## 943 2020-04-27 South East 41
## 944 2020-04-28 South East 40
## 945 2020-04-29 South East 47
## 946 2020-04-30 South East 29
## 947 2020-05-01 South East 37
## 948 2020-05-02 South East 36
## 949 2020-05-03 South East 17
## 950 2020-05-04 South East 35
## 951 2020-05-05 South East 29
## 952 2020-05-06 South East 25
## 953 2020-05-07 South East 27
## 954 2020-05-08 South East 26
## 955 2020-05-09 South East 28
## 956 2020-05-10 South East 19
## 957 2020-05-11 South East 25
## 958 2020-05-12 South East 27
## 959 2020-05-13 South East 18
## 960 2020-05-14 South East 32
## 961 2020-05-15 South East 25
## 962 2020-05-16 South East 22
## 963 2020-05-17 South East 18
## 964 2020-05-18 South East 22
## 965 2020-05-19 South East 12
## 966 2020-05-20 South East 22
## 967 2020-05-21 South East 15
## 968 2020-05-22 South East 17
## 969 2020-05-23 South East 21
## 970 2020-05-24 South East 17
## 971 2020-05-25 South East 13
## 972 2020-05-26 South East 19
## 973 2020-05-27 South East 19
## 974 2020-05-28 South East 12
## 975 2020-05-29 South East 22
## 976 2020-05-30 South East 8
## 977 2020-05-31 South East 12
## 978 2020-06-01 South East 11
## 979 2020-06-02 South East 13
## 980 2020-06-03 South East 18
## 981 2020-06-04 South East 11
## 982 2020-06-05 South East 11
## 983 2020-06-06 South East 10
## 984 2020-06-07 South East 12
## 985 2020-06-08 South East 8
## 986 2020-06-09 South East 10
## 987 2020-06-10 South East 11
## 988 2020-06-11 South East 5
## 989 2020-06-12 South East 6
## 990 2020-06-13 South East 7
## 991 2020-06-14 South East 7
## 992 2020-06-15 South East 8
## 993 2020-06-16 South East 14
## 994 2020-06-17 South East 9
## 995 2020-06-18 South East 4
## 996 2020-06-19 South East 7
## 997 2020-06-20 South East 5
## 998 2020-06-21 South East 3
## 999 2020-06-22 South East 2
## 1000 2020-06-23 South East 8
## 1001 2020-06-24 South East 7
## 1002 2020-06-25 South East 5
## 1003 2020-06-26 South East 8
## 1004 2020-06-27 South East 9
## 1005 2020-06-28 South East 6
## 1006 2020-06-29 South East 5
## 1007 2020-06-30 South East 5
## 1008 2020-07-01 South East 2
## 1009 2020-07-02 South East 8
## 1010 2020-07-03 South East 3
## 1011 2020-07-04 South East 6
## 1012 2020-07-05 South East 5
## 1013 2020-07-06 South East 4
## 1014 2020-07-07 South East 6
## 1015 2020-07-08 South East 3
## 1016 2020-07-09 South East 7
## 1017 2020-07-10 South East 3
## 1018 2020-07-11 South East 4
## 1019 2020-07-12 South East 4
## 1020 2020-07-13 South East 5
## 1021 2020-07-14 South East 5
## 1022 2020-07-15 South East 6
## 1023 2020-07-16 South East 3
## 1024 2020-07-17 South East 1
## 1025 2020-07-18 South East 5
## 1026 2020-07-19 South East 2
## 1027 2020-07-20 South East 6
## 1028 2020-07-21 South East 4
## 1029 2020-07-22 South East 2
## 1030 2020-07-23 South East 3
## 1031 2020-07-24 South East 1
## 1032 2020-07-25 South East 1
## 1033 2020-07-26 South East 3
## 1034 2020-07-27 South East 1
## 1035 2020-07-28 South East 3
## 1036 2020-07-29 South East 2
## 1037 2020-07-30 South East 3
## 1038 2020-07-31 South East 1
## 1039 2020-08-01 South East 2
## 1040 2020-08-02 South East 3
## 1041 2020-08-03 South East 0
## 1042 2020-08-04 South East 0
## 1043 2020-08-05 South East 0
## 1044 2020-08-06 South East 2
## 1045 2020-08-07 South East 0
## 1046 2020-08-08 South East 2
## 1047 2020-08-09 South East 0
## 1048 2020-08-10 South East 1
## 1049 2020-08-11 South East 1
## 1050 2020-08-12 South East 1
## 1051 2020-08-13 South East 0
## 1052 2020-08-14 South East 0
## 1053 2020-08-15 South East 1
## 1054 2020-08-16 South East 1
## 1055 2020-08-17 South East 0
## 1056 2020-08-18 South East 1
## 1057 2020-08-19 South East 0
## 1058 2020-08-20 South East 0
## 1059 2020-08-21 South East 0
## 1060 2020-08-22 South East 0
## 1061 2020-08-23 South East 1
## 1062 2020-08-24 South East 0
## 1063 2020-03-01 South West 0
## 1064 2020-03-02 South West 0
## 1065 2020-03-03 South West 0
## 1066 2020-03-04 South West 0
## 1067 2020-03-05 South West 0
## 1068 2020-03-06 South West 0
## 1069 2020-03-07 South West 0
## 1070 2020-03-08 South West 0
## 1071 2020-03-09 South West 0
## 1072 2020-03-10 South West 0
## 1073 2020-03-11 South West 1
## 1074 2020-03-12 South West 0
## 1075 2020-03-13 South West 0
## 1076 2020-03-14 South West 1
## 1077 2020-03-15 South West 0
## 1078 2020-03-16 South West 0
## 1079 2020-03-17 South West 2
## 1080 2020-03-18 South West 2
## 1081 2020-03-19 South West 4
## 1082 2020-03-20 South West 3
## 1083 2020-03-21 South West 6
## 1084 2020-03-22 South West 7
## 1085 2020-03-23 South West 8
## 1086 2020-03-24 South West 7
## 1087 2020-03-25 South West 9
## 1088 2020-03-26 South West 11
## 1089 2020-03-27 South West 13
## 1090 2020-03-28 South West 21
## 1091 2020-03-29 South West 18
## 1092 2020-03-30 South West 23
## 1093 2020-03-31 South West 23
## 1094 2020-04-01 South West 21
## 1095 2020-04-02 South West 23
## 1096 2020-04-03 South West 30
## 1097 2020-04-04 South West 42
## 1098 2020-04-05 South West 32
## 1099 2020-04-06 South West 34
## 1100 2020-04-07 South West 39
## 1101 2020-04-08 South West 47
## 1102 2020-04-09 South West 24
## 1103 2020-04-10 South West 46
## 1104 2020-04-11 South West 43
## 1105 2020-04-12 South West 23
## 1106 2020-04-13 South West 27
## 1107 2020-04-14 South West 24
## 1108 2020-04-15 South West 32
## 1109 2020-04-16 South West 29
## 1110 2020-04-17 South West 33
## 1111 2020-04-18 South West 25
## 1112 2020-04-19 South West 31
## 1113 2020-04-20 South West 26
## 1114 2020-04-21 South West 26
## 1115 2020-04-22 South West 23
## 1116 2020-04-23 South West 17
## 1117 2020-04-24 South West 19
## 1118 2020-04-25 South West 15
## 1119 2020-04-26 South West 27
## 1120 2020-04-27 South West 13
## 1121 2020-04-28 South West 17
## 1122 2020-04-29 South West 15
## 1123 2020-04-30 South West 26
## 1124 2020-05-01 South West 6
## 1125 2020-05-02 South West 7
## 1126 2020-05-03 South West 10
## 1127 2020-05-04 South West 17
## 1128 2020-05-05 South West 14
## 1129 2020-05-06 South West 19
## 1130 2020-05-07 South West 16
## 1131 2020-05-08 South West 6
## 1132 2020-05-09 South West 11
## 1133 2020-05-10 South West 5
## 1134 2020-05-11 South West 8
## 1135 2020-05-12 South West 7
## 1136 2020-05-13 South West 7
## 1137 2020-05-14 South West 6
## 1138 2020-05-15 South West 4
## 1139 2020-05-16 South West 4
## 1140 2020-05-17 South West 6
## 1141 2020-05-18 South West 4
## 1142 2020-05-19 South West 6
## 1143 2020-05-20 South West 1
## 1144 2020-05-21 South West 9
## 1145 2020-05-22 South West 7
## 1146 2020-05-23 South West 6
## 1147 2020-05-24 South West 3
## 1148 2020-05-25 South West 8
## 1149 2020-05-26 South West 11
## 1150 2020-05-27 South West 5
## 1151 2020-05-28 South West 10
## 1152 2020-05-29 South West 7
## 1153 2020-05-30 South West 3
## 1154 2020-05-31 South West 2
## 1155 2020-06-01 South West 7
## 1156 2020-06-02 South West 2
## 1157 2020-06-03 South West 7
## 1158 2020-06-04 South West 2
## 1159 2020-06-05 South West 2
## 1160 2020-06-06 South West 1
## 1161 2020-06-07 South West 3
## 1162 2020-06-08 South West 3
## 1163 2020-06-09 South West 0
## 1164 2020-06-10 South West 1
## 1165 2020-06-11 South West 2
## 1166 2020-06-12 South West 2
## 1167 2020-06-13 South West 2
## 1168 2020-06-14 South West 0
## 1169 2020-06-15 South West 2
## 1170 2020-06-16 South West 2
## 1171 2020-06-17 South West 0
## 1172 2020-06-18 South West 0
## 1173 2020-06-19 South West 0
## 1174 2020-06-20 South West 2
## 1175 2020-06-21 South West 0
## 1176 2020-06-22 South West 1
## 1177 2020-06-23 South West 1
## 1178 2020-06-24 South West 1
## 1179 2020-06-25 South West 0
## 1180 2020-06-26 South West 3
## 1181 2020-06-27 South West 0
## 1182 2020-06-28 South West 0
## 1183 2020-06-29 South West 1
## 1184 2020-06-30 South West 0
## 1185 2020-07-01 South West 0
## 1186 2020-07-02 South West 0
## 1187 2020-07-03 South West 0
## 1188 2020-07-04 South West 0
## 1189 2020-07-05 South West 1
## 1190 2020-07-06 South West 0
## 1191 2020-07-07 South West 0
## 1192 2020-07-08 South West 2
## 1193 2020-07-09 South West 0
## 1194 2020-07-10 South West 1
## 1195 2020-07-11 South West 0
## 1196 2020-07-12 South West 0
## 1197 2020-07-13 South West 1
## 1198 2020-07-14 South West 0
## 1199 2020-07-15 South West 0
## 1200 2020-07-16 South West 0
## 1201 2020-07-17 South West 1
## 1202 2020-07-18 South West 0
## 1203 2020-07-19 South West 0
## 1204 2020-07-20 South West 0
## 1205 2020-07-21 South West 0
## 1206 2020-07-22 South West 0
## 1207 2020-07-23 South West 0
## 1208 2020-07-24 South West 0
## 1209 2020-07-25 South West 0
## 1210 2020-07-26 South West 0
## 1211 2020-07-27 South West 0
## 1212 2020-07-28 South West 0
## 1213 2020-07-29 South West 0
## 1214 2020-07-30 South West 1
## 1215 2020-07-31 South West 0
## 1216 2020-08-01 South West 0
## 1217 2020-08-02 South West 0
## 1218 2020-08-03 South West 0
## 1219 2020-08-04 South West 0
## 1220 2020-08-05 South West 0
## 1221 2020-08-06 South West 0
## 1222 2020-08-07 South West 0
## 1223 2020-08-08 South West 0
## 1224 2020-08-09 South West 0
## 1225 2020-08-10 South West 0
## 1226 2020-08-11 South West 0
## 1227 2020-08-12 South West 0
## 1228 2020-08-13 South West 0
## 1229 2020-08-14 South West 1
## 1230 2020-08-15 South West 0
## 1231 2020-08-16 South West 0
## 1232 2020-08-17 South West 2
## 1233 2020-08-18 South West 0
## 1234 2020-08-19 South West 0
## 1235 2020-08-20 South West 0
## 1236 2020-08-21 South West 0
## 1237 2020-08-22 South West 0
## 1238 2020-08-23 South West 0
## 1239 2020-08-24 South West 0We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Tuesday 25 Aug 2020.
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_reportWe plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lagThis analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -17.992 -7.123 -2.147 4.446 11.903
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.183e+00 7.934e-02 52.72 <2e-16 ***
## note_lag 1.794e-05 8.263e-07 21.71 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 43.04814)
##
## Null deviance: 22954.1 on 115 degrees of freedom
## Residual deviance: 5352.7 on 114 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 65.535929 1.000018
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 55.910662 76.31758
## note_lag 1.000016 1.00002
Rsq(lag_mod)
## [1] 0.7668086
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fitThis is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom")
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_regWe save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))The following information documents the system on which the document was compiled.
This provides information on the operating system.
This provides information on the version of R used:
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.2 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.15
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.2.0
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.2 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.2 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.1 tibble_3.0.3 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-148 fs_1.5.0 webshot_0.5.2 httr_1.4.2
## [5] rprojroot_1.3-2 tools_4.0.2 backports_1.1.9 utf8_1.1.4
## [9] R6_2.4.1 mgcv_1.8-31 DBI_1.1.0 colorspace_1.4-1
## [13] withr_2.2.0 gridExtra_2.3 tidyselect_1.1.0 sodium_1.1
## [17] curl_4.3 compiler_4.0.2 cli_2.0.2 labeling_0.3
## [21] matchmaker_0.1.1 scales_1.1.1 digest_0.6.25 foreign_0.8-80
## [25] rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
## [29] htmlwidgets_1.5.1 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
## [33] farver_2.0.3 generics_0.0.2 jsonlite_1.7.0 crosstalk_1.1.0.1
## [37] car_3.0-9 zip_2.1.0 magrittr_1.5 kyotil_2019.11-22
## [41] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
## [45] viridis_0.5.1 abind_1.4-5 lifecycle_0.2.0 stringi_1.4.6
## [49] yaml_2.2.1 carData_3.0-4 snakecase_0.11.0 MASS_7.3-51.6
## [53] plyr_1.8.6 grid_4.0.2 blob_1.2.1 crayon_1.3.4
## [57] lattice_0.20-41 cowplot_1.0.0 splines_4.0.2 haven_2.3.1
## [61] hms_0.5.3 knitr_1.29 pillar_1.4.6 boot_1.3-25
## [65] ggsignif_0.6.0 reprex_0.3.0 glue_1.4.1 evaluate_0.14
## [69] data.table_1.13.0 modelr_0.1.8 vctrs_0.3.2 selectr_0.4-2
## [73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.16
## [77] openxlsx_4.1.5 broom_0.7.0 rstatix_0.6.0 survival_3.1-12
## [81] viridisLite_0.3.0 ellipsis_0.3.1